Diagnosis of Nitrogen Nutrition in Flue-Cured Tobacco Based on UAV Visible Spectrum Platform
SUN Zhi-wei1, WANG Xiao-lin1, ZHANG Qi-ming2, YUAN Ju-min2, ZHANG Shuang1, YAN Hui-feng1*, WANG Shu-sheng1*
1. Tobacco Research Institute of Chinese Academy of Agricultural Sciences, Key Laboratory of Tobacco Biology and Processing, Ministry of Agriculture and Rural Affairs, Qingdao 266101, China
2. Jiangxi Institute of Tobacco Science, Nanchang 330025, China
Abstract：The feasibility of non-destructive assessment and prediction of tobacco nitrogen nutrition based on visible spectrum diagnostic technology on UAV platform was analyzed by field experiments with different nitrogen dosage, and the optimum color parameters and model of the technology were defined. Field experiments were carried out in Anfu, Jiangxi Province in 2018. Different nitrogen dosages were set at 0, 45, 90, 135, 180 and 300 kg N·ha-1. At 47 days after transplantation (cluster stage), 83 days after transplantation (flourishing late stage) and 116 days after transplantation (mature stage of lower leaves), digital images of canopy RGB color were obtained by UAV. At the same time, plant samples were collected to analyze aboveground biomass and leaf biomass. Digital analysis of canopy images was carried out to obtain the value of color indices. Through correlation analysis between color indices and tobacco nitrogen nutrition indices, appropriate color indices were screened and nitrogen nutrition diagnosis equation was established. The fitting accuracy of the diagnostic equation of nitrogen nutrition was validated by the experiment of nitrogen consumption in different plots. The results showed that the standard color values of canopy images were significantly different between different treatments in the later stage of flourishing growth, and there was no significant difference between the clustering stage and the maturity stage of lower leaves. Among the 15 color indexes, NRI, NGI, G/R, G/(R+B), (G-R)/(R+G+B) and ExG were significantly correlated with nitrogen nutrition indexes of 5 flue-cured tobacco varieties (p<0.01). According to the screening method of canopy color index, NGI, G/R and ExG are the potential optimal color parameters in the normalized color index system, the ratio color index system and the normalized difference color index system. According to different types of regression analysis results, exponential regression was determined as the prediction model of aboveground biomass and leaf biomass, and linear regression as the prediction model of aboveground nitrogen concentration, leaf nitrogen concentration and leaf SPAD value. The RMSE values of G/R for aboveground nitrogen concentration and leaf nitrogen concentration were 0.375 1% and 0.249 1%, respectively, significantly lower than NGI and ExG, with the highest prediction accuracy. The prediction equations of above-ground biomass, leaf biomass, above-ground nitrogen concentration, leaf nitrogen concentration and SPAD value expressed by G/R value are Y=21.785e1.350 2G/R, Y=4.057 9e1.937 3G/R, Y=5.039 9G/R-3.333 2, Y=4.281 4G/R-3.802 9, Y=40.168G/R-28.188. Therefore, the UVAs platform-based visible spectrum diagnosis technology has application potential in the nitrogen nutrition diagnosis of flue-cured tobacco. The best evaluation period is the flourishing later stage, and the best prediction parameter is G/R value.
Key words：Flue-cured tobacco; Canopy visible spectrum; Nitrogen diagnosis; Color index; Equation model; Growth period
孙志伟，王晓琳，张启明，苑举民，张 爽，闫慧峰，王树声. 基于无人机可见光谱平台的烤烟氮素营养诊断[J]. 光谱学与光谱分析, 2021, 41(02): 586-591.
SUN Zhi-wei, WANG Xiao-lin, ZHANG Qi-ming, YUAN Ju-min, ZHANG Shuang, YAN Hui-feng, WANG Shu-sheng. Diagnosis of Nitrogen Nutrition in Flue-Cured Tobacco Based on UAV Visible Spectrum Platform. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(02): 586-591.
[1] WANG Xiao-qin, WANG Miao-miao, WANG Shao-qiang, et al(王小钦，王苗苗，王绍强，等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报)，2015，31(5)：152.
[2] Yao Y, Miao Y, Huang S, et al. Agronomy for Sustainable Development, 2012, 32(4): 925.
[3] WANG Yuan, WANG De-jian, ZHANG-Gang, et al(王 远, 王德建, 张 刚, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2012, 28(17): 131.
[4] LI Lan-tao, ZHANG Meng, REN Tao, et al(李岚涛, 张 萌, 任 涛, 等). Journal of Plant Nutrition and Fertilizer(植物营养与肥料学报), 2015, 21(1): 259.
[5] WEI Quan-quan, LI Lan-tao, REN Tao, et al(魏全全, 李岚涛, 任 涛, 等). Scientia Agricultura Sinica(中国农业科学), 2015, 48(19): 3877.
[6] LI Hong-Jun, ZHANG Li-zhou, CHEN Xi-ming, et al(李红军, 张立周, 陈曦鸣, 等). Chinese Journal of Eco-Agriculture(中国生态农业学报), 2011, 19(1): 155.
[7] LIU Chang-hua, MA Wen-yu, CHEN Zhi-chao(刘昌华, 马文玉, 陈志超). Journal of Henan University of Technology·Natural Science Edition(河南理工大学学报·自然科学版), 2018, 37(3): 45.
[8] CHEN Jia-yue, YAO Xia, HUANG Fen, et al(陈佳悦, 姚 霞, 黄 芬, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2016, 32(4): 163.
[9] ZHANG Ling, CHEN Xin-ping, JIA Liang-liang, et al(张 玲, 陈新平, 贾良良, 等). Journal of Plant Nutrition and Fertilizer(植物营养与肥料学报), 2018, 24(1): 261.
[10] LI Jin-wei, GUAN He-qing, LIAO Gui-ping(李锦卫，管鹤卿，廖桂平). Agriculture Network Information(农业网络信息)，2010，12：15.
[11] Yao X, Si H, Chen T, et al. Frontiers in Plant Science, 2018, 9: 1360.
[12] LIU Hong-jian, ZENG Ai-ping, ZHENG Li-min(刘洪见, 曾爱平, 郑丽敏). Agricultural Network Information(农业网络信息), 2007，(12): 31.
[13] TANG Xian-gan，LI Zu-zhang，HU Qi-feng，et al(唐先干, 李祖章, 胡启锋，等). Chinese Tobacco Science(中国烟草科学), 2012, 33(3): 47.
